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    "# 个性化召回算法总结与评价\n",
    "\n",
    "## 总结\n",
    "\n",
    "### 分类\n",
    "\n",
    "- 基于邻域的（CF、LFM、Personal Rank）\n",
    "\n",
    "- 基于内容的（content base）\n",
    "\n",
    "- 基于神经网络的（item2vec）\n",
    "\n",
    "### 多算法并存\n",
    "\n",
    "| 算法\\召回 | item |\n",
    "| - | - |\n",
    "| A | a b |\n",
    "| B | a c d |\n",
    "| C | e f d c |\n",
    "\n",
    "- 如何缺点每个算法的召回数目\n",
    "\n",
    "    1.根据Rank性能确定一个数目，然后根据算法历史表现，按比例分配\n",
    "    \n",
    "    2.Rank阶段无性能压力，全部召回后进行合并。\n",
    "\n",
    "## 评价\n",
    "\n",
    "- 离线评价准入\n",
    "\n",
    "    - 评价新增算法的推荐结果在测试集上的表现\n",
    "\n",
    "- 在线评价收益\n",
    "\n",
    "    - 定义指标\n",
    "    \n",
    "    - 生成环境a b test"
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